Handwriting recognition programs generally operate by comparing data generated from handwritten words or characters and stored recognition data (referred to as a dictionary by those skilled in the art) in an attempt to correctly replicate the handwritten words or characters as typewritten text. As commercial applications for handwriting recognition become more prominent in the marketplace, the trend has been to increase the size of the dictionary to make the recognition program more general in nature so as to be universally useful in various applications.
However, continually increasing the size of the dictionary can lead to diminishing returns in the context of reduced recognition accuracy. Also, a large dictionary can lead to increased recognition processing, which manifests itself to the user as a slower recognition response time. Some more advanced handwriting recognition programs have attempted (with mixed results) to compensate for a large recognition dictionary by including weighting values associated with some or all of the stored recognition data that are used in the recognition process to improve the likelihood of correctly recognizing the handwritten input.
Nevertheless, there exists a need for a handwriting recognition system that may find wide application without compromising recognition accuracy and speed.